analise_transacoes.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1y1XKsy7yMPk"
      },
      "source": [
        "# Leia o arquivo e inspecione os dados"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "cF2ZD0jByMPm"
      },
      "outputs": [],
      "source": [
        "# Importando as bibliotecas\n",
        "import numpy as np\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 538
        },
        "id": "F8VZnB5WyMPn",
        "outputId": "9699abb4-4c68-41f4-d6df-db04b78f1434"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  InvoiceNo StockCode                          Description  Quantity  \\\n",
              "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
              "1    536365     71053                  WHITE METAL LANTERN         6   \n",
              "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
              "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
              "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
              "\n",
              "       InvoiceDate  UnitPrice  CustomerID         Country  \n",
              "0  12/1/2010 08:26       2.55     17850.0  United Kingdom  \n",
              "1  12/1/2010 08:26       3.39     17850.0  United Kingdom  \n",
              "2  12/1/2010 08:26       2.75     17850.0  United Kingdom  \n",
              "3  12/1/2010 08:26       3.39     17850.0  United Kingdom  \n",
              "4  12/1/2010 08:26       3.39     17850.0  United Kingdom  "
            ],
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              "      <td>6</td>\n",
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              "      <td>2.55</td>\n",
              "      <td>17850.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>536365</td>\n",
              "      <td>71053</td>\n",
              "      <td>WHITE METAL LANTERN</td>\n",
              "      <td>6</td>\n",
              "      <td>12/1/2010 08:26</td>\n",
              "      <td>3.39</td>\n",
              "      <td>17850.0</td>\n",
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              "      <th>2</th>\n",
              "      <td>536365</td>\n",
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              "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
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              "      <td>3.39</td>\n",
              "      <td>17850.0</td>\n",
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              "      <th>4</th>\n",
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              "      <td>6</td>\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "type": "dataframe",
              "variable_name": "df"
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          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "# Importando dados\n",
        "df = pd.read_csv('Data.csv', delimiter=',')\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "q5ZwQhVMyMPn"
      },
      "outputs": [],
      "source": [
        "# Limpando espaços em brancos do DataFrame\n",
        "df.columns = [col.strip() for col in df.columns]\n",
        "df = df.applymap(lambda x: x.strip() if isinstance(x, str) else x)\n",
        "df = df.applymap(lambda x: np.nan if x == '' else x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
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        "id": "ipQhiFI5yMPo",
        "outputId": "22badc10-bc57-4be9-f410-f1238e13ab7f"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Quantity      UnitPrice     CustomerID\n",
              "count  541909.000000  541909.000000  406829.000000\n",
              "mean        9.552250       4.611121   15287.690570\n",
              "std       218.081158      96.759853    1713.600303\n",
              "min    -80995.000000  -11062.060000   12346.000000\n",
              "25%         1.000000       1.250000   13953.000000\n",
              "50%         3.000000       2.080000   15152.000000\n",
              "75%        10.000000       4.130000   16791.000000\n",
              "max     80995.000000   38970.000000   18287.000000"
            ],
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              "      <th>count</th>\n",
              "      <td>541909.000000</td>\n",
              "      <td>541909.000000</td>\n",
              "      <td>406829.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>9.552250</td>\n",
              "      <td>4.611121</td>\n",
              "      <td>15287.690570</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
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              "      <th>min</th>\n",
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              "      <td>-11062.060000</td>\n",
              "      <td>12346.000000</td>\n",
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              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.250000</td>\n",
              "      <td>13953.000000</td>\n",
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              "      <th>50%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.080000</td>\n",
              "      <td>15152.000000</td>\n",
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              "      <th>75%</th>\n",
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              "      <td>4.130000</td>\n",
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              "      <th>max</th>\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Quantity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 196412.4226608867,\n        \"min\": -80995.0,\n        \"max\": 541909.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          9.55224954743324,\n          3.0,\n          541909.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"UnitPrice\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 190752.0757073426,\n        \"min\": -11062.06,\n        \"max\": 541909.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          4.611121000020299,\n          2.08,\n          541909.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CustomerID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 139204.16800694188,\n        \"min\": 1713.600303321598,\n        \"max\": 406829.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          15287.690570239585,\n          15152.0,\n          406829.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "# Análisando colunas númericas com describe\n",
        "df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gS4NmroAyMPo",
        "outputId": "610ff1ac-081b-4959-b3fa-989c6724e2b3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 541909 entries, 0 to 541908\n",
            "Data columns (total 8 columns):\n",
            " #   Column       Non-Null Count   Dtype  \n",
            "---  ------       --------------   -----  \n",
            " 0   InvoiceNo    541909 non-null  object \n",
            " 1   StockCode    541909 non-null  object \n",
            " 2   Description  540455 non-null  object \n",
            " 3   Quantity     541909 non-null  int64  \n",
            " 4   InvoiceDate  541909 non-null  object \n",
            " 5   UnitPrice    541909 non-null  float64\n",
            " 6   CustomerID   406829 non-null  float64\n",
            " 7   Country      541909 non-null  object \n",
            "dtypes: float64(2), int64(1), object(5)\n",
            "memory usage: 33.1+ MB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uMVqGhe5yMPo"
      },
      "source": [
        "# Valores faltantes na identificação do cliente"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gOgYo86wyMPo",
        "outputId": "463414ac-f299-4c46-a659-6d3141dc2590"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "135080"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "# Valores faltantes na identificação do cliente\n",
        "df['CustomerID'].isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "iKEWPXtLyMPo"
      },
      "outputs": [],
      "source": [
        "# Apagando linhas de clientes NaN\n",
        "df.dropna(subset=['CustomerID'], inplace=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UOuGGKz3yMPo"
      },
      "source": [
        "# Preços unitários e quantidade de produtos iguais ou inferior a 0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 877
        },
        "id": "99HkUZOzyMPo",
        "outputId": "b269ef4c-1458-4cee-e532-21621b78767d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       InvoiceNo StockCode                       Description  Quantity  \\\n",
              "141      C536379         D                          Discount        -1   \n",
              "154      C536383    35004C   SET OF 3 COLOURED  FLYING DUCKS        -1   \n",
              "235      C536391     22556     PLASTERS IN TIN CIRCUS PARADE       -12   \n",
              "236      C536391     21984   PACK OF 12 PINK PAISLEY TISSUES       -24   \n",
              "237      C536391     21983   PACK OF 12 BLUE PAISLEY TISSUES       -24   \n",
              "...          ...       ...                               ...       ...   \n",
              "540449   C581490     23144   ZINC T-LIGHT HOLDER STARS SMALL       -11   \n",
              "541541   C581499         M                            Manual        -1   \n",
              "541715   C581568     21258        VICTORIAN SEWING BOX LARGE        -5   \n",
              "541716   C581569     84978  HANGING HEART JAR T-LIGHT HOLDER        -1   \n",
              "541717   C581569     20979     36 PENCILS TUBE RED RETROSPOT        -5   \n",
              "\n",
              "            InvoiceDate  UnitPrice  CustomerID         Country  \n",
              "141     12/1/2010 09:41      27.50     14527.0  United Kingdom  \n",
              "154     12/1/2010 09:49       4.65     15311.0  United Kingdom  \n",
              "235     12/1/2010 10:24       1.65     17548.0  United Kingdom  \n",
              "236     12/1/2010 10:24       0.29     17548.0  United Kingdom  \n",
              "237     12/1/2010 10:24       0.29     17548.0  United Kingdom  \n",
              "...                 ...        ...         ...             ...  \n",
              "540449  12/9/2011 09:57       0.83     14397.0  United Kingdom  \n",
              "541541  12/9/2011 10:28     224.69     15498.0  United Kingdom  \n",
              "541715  12/9/2011 11:57      10.95     15311.0  United Kingdom  \n",
              "541716  12/9/2011 11:58       1.25     17315.0  United Kingdom  \n",
              "541717  12/9/2011 11:58       1.25     17315.0  United Kingdom  \n",
              "\n",
              "[8945 rows x 8 columns]"
            ],
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              "        document.querySelector('#df-5eb11086-8618-4f87-a8ff-6bbf70ae773e button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-5eb11086-8618-4f87-a8ff-6bbf70ae773e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-26cbc01a-8833-44e3-b28a-7478de966060\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-26cbc01a-8833-44e3-b28a-7478de966060')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-26cbc01a-8833-44e3-b28a-7478de966060 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "repr_error": "0"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "# Verificando a existência de Preços unitários e Quantidades iguais ou abaixo de 0\n",
        "df.query('UnitPrice <= 0 or Quantity <= 0')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "oHP-y3ozyMPo"
      },
      "outputs": [],
      "source": [
        "# Filtrando DataFrame para utilizar apenas os Preços unitários e Quantidades acima de 0\n",
        "df.query('UnitPrice > 0 and Quantity > 0', inplace=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "33LzA9yUyMPp"
      },
      "source": [
        "# Verifique se existem linhas duplicadas"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nEByGSzlyMPp",
        "outputId": "2fcd78a9-c362-472b-8416-fb6980fdc8e1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5192"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "# Verificando linhas duplicadas\n",
        "df.duplicated().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "BjtnT8TIyMPp"
      },
      "outputs": [],
      "source": [
        "# Retirando linhas duplicadas\n",
        "df.drop_duplicates(inplace=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z6WSlW3eyMPp"
      },
      "source": [
        "# Tipos de dados da coluna"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nFNmWKQByMPp",
        "outputId": "8359726a-b2a4-4a98-8e95-a69c79db5c4c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 392692 entries, 0 to 541908\n",
            "Data columns (total 8 columns):\n",
            " #   Column       Non-Null Count   Dtype  \n",
            "---  ------       --------------   -----  \n",
            " 0   InvoiceNo    392692 non-null  object \n",
            " 1   StockCode    392692 non-null  object \n",
            " 2   Description  392692 non-null  object \n",
            " 3   Quantity     392692 non-null  int64  \n",
            " 4   InvoiceDate  392692 non-null  object \n",
            " 5   UnitPrice    392692 non-null  float64\n",
            " 6   CustomerID   392692 non-null  float64\n",
            " 7   Country      392692 non-null  object \n",
            "dtypes: float64(2), int64(1), object(5)\n",
            "memory usage: 27.0+ MB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 538
        },
        "id": "-FzzQzDoyMPp",
        "outputId": "f4939785-db85-4ff8-e14c-ec1b089b70f4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  InvoiceNo StockCode                          Description  Quantity  \\\n",
              "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
              "1    536365     71053                  WHITE METAL LANTERN         6   \n",
              "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
              "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
              "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
              "\n",
              "       InvoiceDate  UnitPrice  CustomerID         Country  \n",
              "0  12/1/2010 08:26       2.55     17850.0  United Kingdom  \n",
              "1  12/1/2010 08:26       3.39     17850.0  United Kingdom  \n",
              "2  12/1/2010 08:26       2.75     17850.0  United Kingdom  \n",
              "3  12/1/2010 08:26       3.39     17850.0  United Kingdom  \n",
              "4  12/1/2010 08:26       3.39     17850.0  United Kingdom  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3bb99e9a-7586-461f-9076-80f88147bec2\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>InvoiceNo</th>\n",
              "      <th>StockCode</th>\n",
              "      <th>Description</th>\n",
              "      <th>Quantity</th>\n",
              "      <th>InvoiceDate</th>\n",
              "      <th>UnitPrice</th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Country</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>536365</td>\n",
              "      <td>85123A</td>\n",
              "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
              "      <td>6</td>\n",
              "      <td>12/1/2010 08:26</td>\n",
              "      <td>2.55</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>536365</td>\n",
              "      <td>71053</td>\n",
              "      <td>WHITE METAL LANTERN</td>\n",
              "      <td>6</td>\n",
              "      <td>12/1/2010 08:26</td>\n",
              "      <td>3.39</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>536365</td>\n",
              "      <td>84406B</td>\n",
              "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
              "      <td>8</td>\n",
              "      <td>12/1/2010 08:26</td>\n",
              "      <td>2.75</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>536365</td>\n",
              "      <td>84029G</td>\n",
              "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
              "      <td>6</td>\n",
              "      <td>12/1/2010 08:26</td>\n",
              "      <td>3.39</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>536365</td>\n",
              "      <td>84029E</td>\n",
              "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
              "      <td>6</td>\n",
              "      <td>12/1/2010 08:26</td>\n",
              "      <td>3.39</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3bb99e9a-7586-461f-9076-80f88147bec2')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-3bb99e9a-7586-461f-9076-80f88147bec2 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-3bb99e9a-7586-461f-9076-80f88147bec2');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-7f4c64ad-ae26-4179-af4e-4b75818dfc07\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7f4c64ad-ae26-4179-af4e-4b75818dfc07')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7f4c64ad-ae26-4179-af4e-4b75818dfc07 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "tfrSPtYbyMPp"
      },
      "outputs": [],
      "source": [
        "# Alterando o InvoiceDate para tipo datetime64[ns]\n",
        "df['InvoiceDate'] = pd.to_datetime(df['InvoiceDate'], format='%m/%d/%Y %H:%M')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "-26pQN2FyMPp"
      },
      "outputs": [],
      "source": [
        "# Alterando o CustomerID para o tipo int64\n",
        "df['CustomerID'] = df['CustomerID'].astype(int)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yn6N4v9SyMPq",
        "outputId": "50ad23e1-b8b4-46a6-cacb-60ae34ca1bbe"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 392692 entries, 0 to 541908\n",
            "Data columns (total 8 columns):\n",
            " #   Column       Non-Null Count   Dtype         \n",
            "---  ------       --------------   -----         \n",
            " 0   InvoiceNo    392692 non-null  object        \n",
            " 1   StockCode    392692 non-null  object        \n",
            " 2   Description  392692 non-null  object        \n",
            " 3   Quantity     392692 non-null  int64         \n",
            " 4   InvoiceDate  392692 non-null  datetime64[ns]\n",
            " 5   UnitPrice    392692 non-null  float64       \n",
            " 6   CustomerID   392692 non-null  int64         \n",
            " 7   Country      392692 non-null  object        \n",
            "dtypes: datetime64[ns](1), float64(1), int64(2), object(4)\n",
            "memory usage: 27.0+ MB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hqeNju3fyMPq"
      },
      "source": [
        "# Tratando os outliers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "mxQbgWM3yMPq"
      },
      "outputs": [],
      "source": [
        "# Importando o scale da sub-modulo preprocessing da biblioteca scikit-learn\n",
        "from sklearn.preprocessing import scale"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "JDkWy8_CyMPq",
        "outputId": "5febb87c-8a28-44c5-ddc3-ecbd6d0fbaea"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x500 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "# Plotando visualização dos outilers\n",
        "numeric_cols = df.select_dtypes('number').columns\n",
        "\n",
        "df[numeric_cols].apply(scale).plot.box(figsize=(18, 5));"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "GgrfdqpjyMPq"
      },
      "outputs": [],
      "source": [
        "# Removendo os outilers do Dataframe\n",
        "df.query('Quantity <= 10000 and UnitPrice <= 5000', inplace=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Idug_RxuyMPq"
      },
      "source": [
        "# Crie uma coluna adicional"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 538
        },
        "id": "GxG1snkYyMPq",
        "outputId": "fc311d61-4fab-4ffd-a08f-a048a8326d74"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  InvoiceNo StockCode                          Description  Quantity  \\\n",
              "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
              "1    536365     71053                  WHITE METAL LANTERN         6   \n",
              "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
              "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
              "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
              "\n",
              "          InvoiceDate  UnitPrice  CustomerID         Country  TotalPrice  \n",
              "0 2010-12-01 08:26:00       2.55       17850  United Kingdom       15.30  \n",
              "1 2010-12-01 08:26:00       3.39       17850  United Kingdom       20.34  \n",
              "2 2010-12-01 08:26:00       2.75       17850  United Kingdom       22.00  \n",
              "3 2010-12-01 08:26:00       3.39       17850  United Kingdom       20.34  \n",
              "4 2010-12-01 08:26:00       3.39       17850  United Kingdom       20.34  "
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>InvoiceNo</th>\n",
              "      <th>StockCode</th>\n",
              "      <th>Description</th>\n",
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              "      <th>InvoiceDate</th>\n",
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              "      <td>536365</td>\n",
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              "      <td>6</td>\n",
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              "      <td>2.55</td>\n",
              "      <td>17850</td>\n",
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              "      <td>15.30</td>\n",
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              "      <th>1</th>\n",
              "      <td>536365</td>\n",
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              "      <td>3.39</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>536365</td>\n",
              "      <td>84406B</td>\n",
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              "      <td>8</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>2.75</td>\n",
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              "      <th>3</th>\n",
              "      <td>536365</td>\n",
              "      <td>84029G</td>\n",
              "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
              "      <td>6</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>3.39</td>\n",
              "      <td>17850</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>20.34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>536365</td>\n",
              "      <td>84029E</td>\n",
              "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
              "      <td>6</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>3.39</td>\n",
              "      <td>17850</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>20.34</td>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-a75b0562-a7b4-412c-9660-ea5e8cae8460 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-a75b0562-a7b4-412c-9660-ea5e8cae8460');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-3a38faa6-9234-45f6-b0a3-0375f066669b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3a38faa6-9234-45f6-b0a3-0375f066669b')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3a38faa6-9234-45f6-b0a3-0375f066669b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "# Criando uma coluna de Preço Total\n",
        "df['TotalPrice'] =  df['Quantity'] * df['UnitPrice']\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6kVPEk2myMPq"
      },
      "source": [
        "# Última data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cni8v8u9yMPq",
        "outputId": "bc71b54f-3ede-4016-ced3-221d15205fd2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Timestamp('2011-12-09 12:50:00')"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "# Verificando a ultima data de venda\n",
        "end_date = df['InvoiceDate'].max()\n",
        "end_date"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fIZvwYaIyMPq"
      },
      "source": [
        "# Plotando gráficos"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "ZQSUnNI2yMPq"
      },
      "outputs": [],
      "source": [
        "# Importando a biblioteca matplotlib\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "id": "Uo6BnDuqyMPr",
        "outputId": "c2284991-c59d-47f7-85d7-ae3b5d80623c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Top 10 países com maior valor em vendas\n",
        "paises_maior_vendas = df.groupby('Country')['TotalPrice'].sum().sort_values(ascending=False).nlargest(10) / 1000000\n",
        "\n",
        "paises_maior_vendas.plot.barh()\n",
        "plt.title('Top 10 países com maior valor em vendas')\n",
        "plt.ylabel('Países')\n",
        "plt.xlabel('Faturamento Total em Milhões')\n",
        "plt.gca().invert_yaxis()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "id": "PJxdPEEJyMPr",
        "outputId": "f448ea10-707a-4e98-ba70-68bfe04ec2ab"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Top 10 produtos mais vendidos\n",
        "produtos_mais_vendas = df.groupby('Description')['Quantity'].sum().sort_values(ascending=False).nlargest(10)\n",
        "\n",
        "produtos_mais_vendas.plot.barh()\n",
        "plt.title('Top 10 produtos mais vendidos')\n",
        "plt.ylabel('Produtos')\n",
        "plt.xlabel('Qtd.')\n",
        "plt.gca().invert_yaxis()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        },
        "id": "RXuzMW_WyMPr",
        "outputId": "79b4dc57-ced4-4e52-84d9-77cc3a3cac33"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Valor de venda total por mês\n",
        "valor_mes = df.groupby(df['InvoiceDate'].dt.to_period('M'))['TotalPrice'].sum() / 1000000\n",
        "\n",
        "valor_mes.plot.bar()\n",
        "plt.title('Valor de venda total por mês')\n",
        "plt.ylabel('Valor em Milhões')\n",
        "plt.xlabel('Data do Faturamento')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 330
        },
        "id": "6Qx3JUPCyMPr",
        "outputId": "65f948d6-9e98-43da-ddb5-3b2accea33df"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Valor de venda total por mês e por país (considere apenas os top 10)\n",
        "valor_pais_mes = df.groupby(['Country', df['InvoiceDate'].dt.to_period('M')])['TotalPrice'].sum().reset_index()\n",
        "\n",
        "valor_pais_mes.query(f'Country in {list(paises_maior_vendas.reset_index()[\"Country\"])}', inplace=True)\n",
        "\n",
        "valor_pais_mes['InvoiceDate'] = valor_pais_mes['InvoiceDate'].dt.to_timestamp()\n",
        "\n",
        "plt.figure(figsize=(20, 10))\n",
        "\n",
        "for country, data in valor_pais_mes.groupby('Country'):\n",
        "    plt.plot(data['InvoiceDate'], data['TotalPrice'], marker='o', linestyle='-', label=country)\n",
        "\n",
        "plt.title('Faturamento Mensal por País (Top 10)')\n",
        "plt.xlabel('Data do Faturamento')\n",
        "plt.ylabel('Total Faturado')\n",
        "plt.legend()\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MC8goc2PyMPr"
      },
      "source": [
        "# Cálculo do RFM"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 538
        },
        "id": "6RwvF-BUyMPs",
        "outputId": "aefe18c8-a2f6-47a6-d47c-66de7cb6b163"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  InvoiceNo StockCode                          Description  Quantity  \\\n",
              "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
              "1    536365     71053                  WHITE METAL LANTERN         6   \n",
              "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
              "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
              "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
              "\n",
              "          InvoiceDate  UnitPrice  CustomerID         Country  TotalPrice  \n",
              "0 2010-12-01 08:26:00       2.55       17850  United Kingdom       15.30  \n",
              "1 2010-12-01 08:26:00       3.39       17850  United Kingdom       20.34  \n",
              "2 2010-12-01 08:26:00       2.75       17850  United Kingdom       22.00  \n",
              "3 2010-12-01 08:26:00       3.39       17850  United Kingdom       20.34  \n",
              "4 2010-12-01 08:26:00       3.39       17850  United Kingdom       20.34  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8de3a4a4-7a0d-4869-a797-19a724e2152a\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
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              "      <th>InvoiceNo</th>\n",
              "      <th>StockCode</th>\n",
              "      <th>Description</th>\n",
              "      <th>Quantity</th>\n",
              "      <th>InvoiceDate</th>\n",
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              "      <td>6</td>\n",
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              "      <td>2.55</td>\n",
              "      <td>17850</td>\n",
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              "      <td>15.30</td>\n",
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              "      <td>536365</td>\n",
              "      <td>71053</td>\n",
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              "      <td>3.39</td>\n",
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              "      <td>United Kingdom</td>\n",
              "      <td>20.34</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>536365</td>\n",
              "      <td>84406B</td>\n",
              "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
              "      <td>8</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>2.75</td>\n",
              "      <td>17850</td>\n",
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              "    <tr>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>536365</td>\n",
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              "      <td>6</td>\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "c2RpzJaVyMPs"
      },
      "outputs": [],
      "source": [
        "# Função que calcula o RFM\n",
        "def RFM(df, end_date):\n",
        "    r = (end_date - df['InvoiceDate'].max()).days\n",
        "    f = df['InvoiceNo'].count()\n",
        "    m = df['TotalPrice'].sum() / f\n",
        "\n",
        "    return pd.Series({\n",
        "        'InvoiceDate': df['InvoiceDate'].max(),\n",
        "        'TotalPrice': df['TotalPrice'].sum(),\n",
        "        'Recência_(dias)': r,\n",
        "        'Frequência': f,\n",
        "        'TicketMédio': m\n",
        "    })"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "VL2u53RZyMPs",
        "outputId": "3cee3f31-54fc-42e9-d851-89e576ed0ff1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      CustomerID         InvoiceDate  TotalPrice  Recência_(dias)  Frequência  \\\n",
              "0          12347 2011-12-07 15:52:00     4310.00                1           7   \n",
              "1          12348 2011-09-25 13:13:00     1797.24               74           4   \n",
              "2          12349 2011-11-21 09:51:00     1757.55               18           1   \n",
              "3          12350 2011-02-02 16:01:00      334.40              309           1   \n",
              "4          12352 2011-11-03 14:37:00     2506.04               35           8   \n",
              "...          ...                 ...         ...              ...         ...   \n",
              "4332       18280 2011-03-07 09:52:00      180.60              277           1   \n",
              "4333       18281 2011-06-12 10:53:00       80.82              180           1   \n",
              "4334       18282 2011-12-02 11:43:00      178.05                7           2   \n",
              "4335       18283 2011-12-06 12:02:00     2045.53                3          16   \n",
              "4336       18287 2011-10-28 09:29:00     1837.28               42           3   \n",
              "\n",
              "      TicketMédio  \n",
              "0      615.714286  \n",
              "1      449.310000  \n",
              "2     1757.550000  \n",
              "3      334.400000  \n",
              "4      313.255000  \n",
              "...           ...  \n",
              "4332   180.600000  \n",
              "4333    80.820000  \n",
              "4334    89.025000  \n",
              "4335   127.845625  \n",
              "4336   612.426667  \n",
              "\n",
              "[4337 rows x 6 columns]"
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              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-7dfeed12-5a8e-4697-996b-4aacd3419fe8 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-7dfeed12-5a8e-4697-996b-4aacd3419fe8');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-6f44078b-5923-477b-b1ec-3afa88a626ce\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6f44078b-5923-477b-b1ec-3afa88a626ce')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6f44078b-5923-477b-b1ec-3afa88a626ce button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_c0f0ac81-e6a5-4ef9-8596-56088ef22a90\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('cliente_rfm')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_c0f0ac81-e6a5-4ef9-8596-56088ef22a90 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('cliente_rfm');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "cliente_rfm",
              "summary": "{\n  \"name\": \"cliente_rfm\",\n  \"rows\": 4337,\n  \"fields\": [\n    {\n      \"column\": \"CustomerID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1721,\n        \"min\": 12347,\n        \"max\": 18287,\n        \"num_unique_values\": 4337,\n        \"samples\": [\n          17786,\n          14321,\n          15978\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"InvoiceDate\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2010-12-01 09:53:00\",\n        \"max\": \"2011-12-09 12:50:00\",\n        \"num_unique_values\": 4198,\n        \"samples\": [\n          \"2011-10-20 13:13:00\",\n          \"2011-12-01 11:51:00\",\n          \"2011-11-15 16:30:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TotalPrice\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8531.13635371291,\n        \"min\": 2.9,\n        \"max\": 280206.01999999996,\n        \"num_unique_values\": 4261,\n        \"samples\": [\n          1060.25,\n          2703.23,\n          103.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rec\\u00eancia_(dias)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 99,\n        \"min\": 0,\n        \"max\": 373,\n        \"num_unique_values\": 349,\n        \"samples\": [\n          36,\n          117,\n          68\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Frequ\\u00eancia\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7,\n        \"min\": 1,\n        \"max\": 209,\n        \"num_unique_values\": 58,\n        \"samples\": [\n          7,\n          2,\n          62\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TicketM\\u00e9dio\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 497.4340100526795,\n        \"min\": 2.9,\n        \"max\": 14844.766666666668,\n        \"num_unique_values\": 4268,\n        \"samples\": [\n          1797.131875,\n          1022.592,\n          427.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "# Agrupando clientes por RFM\n",
        "cliente_valor = df.groupby(['CustomerID', 'InvoiceNo']).agg({'InvoiceDate': 'max', 'TotalPrice': 'sum'}).reset_index()\n",
        "\n",
        "cliente_rfm = (\n",
        "    cliente_valor\n",
        "    .groupby('CustomerID')\n",
        "    .apply(lambda df: RFM(df, end_date))\n",
        "    .reset_index()\n",
        ")\n",
        "\n",
        "cliente_rfm"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "venv",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.12.3"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}